Many issues are now arising that render a low power remote asset monitoring system desirable. Some of these issues developed from the terrorist threat to the United States since Sep. 11, 2001, and the concern of anti-terrorist personnel with the relatively free and unmonitored transportation of massive amounts of material throughout the United States by trains, trucks, and ships. A system that permits monitoring of the contents of these shipping containers could substantially reduce this terrorist threat.
The FBI has recently stated that cargo crime is conservatively estimated at about $12 billion per year. It is the fastest growing crime problem in the United States. Other areas of criminal activity involve shipments imported into the United States that are used to conceal illegal goods including weapons, illegal immigrants, narcotics, and products that violate trademarks and patents. The recent concern on the potential use of cargo containers as weapons of mass destruction is also causing great pressure to improve information, inspection, tracking and monitoring technologies. Furthermore, the movement of hazardous cargo and the potential for sabotage is also causing increased concern among law enforcement agencies and resulting in increasing demands for security for such hazardous cargo shipments.
A low cost low power monitoring system of cargo containers and their contents could substantially solve these problems.
Cargo security is defined as the safe and reliable intermodal movement of goods from the shipper to the eventual destination with no loss due to theft or damage. Cargo security is concerned with the key assets that move the cargo including containers, trailers, chassis, tractors, vessels and rail cars as well as the cargo itself. Modern manufacturing methods requiring just-in-time delivery further places a premium on cargo security.
The recent increase in cargo theft and the concern for homeland security are thus placing new demands on cargo security and because of the large number of carriers and storage locations, inexpensive systems are needed to continuously monitor the status of cargo from the time that it leaves the shipper until it reaches its final destination. Technological advancements such as the global positioning system (GPS), and improved communication systems, including wireless telecommunications via satellites, and the Internet have created a situation where such an inexpensive system is now possible.
To partially respond to these concerns, projects are underway to remotely monitor the geographic location of shipping containers as well as the tractors and chassis, boats, planes and railroad cars that move these containers or cargo in general. The ability exists now for communicating limited amounts of information from shipping containers directly to central computers and the Internet using satellites and other telematics communication devices.
In some prior art systems, cargo containers are sealed with electronic cargo seals, the integrity of which can be remotely monitored. Knowledge of the container's location as well as the seal integrity are vital pieces of information that can contribute to solving the problems mentioned above. However, this is not sufficient and the addition of various sensors and remote monitoring of these sensors is now not only possible but necessary.
Emerging technology now permits the monitoring of some safety and status information on the chassis such as tire pressures, brake system status, lights, geographical location, generator performance, and container security and this information can now be telecommunicated to a remote location. This invention is concerned with these additional improvements to the remote reporting system.
Additionally, biometric information can be used to validate drivers of vehicles containing hazardous cargo to minimize terrorist activities involving these materials. This data needs to be available remotely especially if there is a sudden change in drivers. Similarly, any deviation from the authorized route can now be detected and this also needs to be remotely reported. Much of the above-mentioned prior art activity is in bits and pieces, that is, it is available on the vehicle and sometimes to the dispatching station while the vehicle is on the premises. It now needs to be available to a central monitoring location at all times. Homeland security issues arising out the components that make up the cargo transportation system including tractors, trailers, chassis, containers and railroad cars, will only be eliminated when the contents of all such elements are known, monitored, and thus the misappropriation of such assets eliminated. The shipping system or process that takes place in the United States should guarantee that all shipping containers contain only the appropriate contents and are always on the proper route from their source to their destination and on schedule. This invention is concerned with achieving this 100 percent system primarily through low power remote monitoring of the assets that make up the shipping system.
The system that is described herein for monitoring shipping assets and the contents of shipping containers can also be used for a variety of other asset monitoring problems including the monitoring of unattended boats, cabins, summer homes, private airplanes, sheds, warehouses, storage facilities and other remote unattended facilities. With additional sensors, the quality of the environment, the integrity of structures, the presence of unwanted contaminants etc. can also now be monitored and reported on an exception basis through a low power, essentially maintenance-free monitoring and reporting system in accordance with the invention as described herein.
Definitions
Preferred embodiments of the invention are described below and unless specifically noted, it is the applicants' intention that the words and phrases in the specification and claims be given the ordinary and accustomed meaning to those of ordinary skill in the applicable art(s). If the applicant intends any other meaning, he will specifically state he is applying a special meaning to a word or phrase.
Likewise, applicants' use of the word “function” here is not intended to indicate that the applicants seek to invoke the special provisions of 35 U.S.C. §112, sixth paragraph, to define their invention. To the contrary, if applicants wish to invoke the provisions of 35 U.S.C. §112, sixth paragraph, to define their invention, they will specifically set forth in the claims the phrases “means for” or “step for” and a function, without also reciting in that phrase any structure, material or act in support of the function. Moreover, even if applicants invoke the provisions of 35 U.S.C. §112, sixth paragraph, to define their invention, it is the applicants' intention that their inventions not be limited to the specific structure, material or acts that are described in the preferred embodiments herein. Rather, if applicants claim their inventions by specifically invoking the provisions of 35 U.S.C. §112, sixth paragraph, it is nonetheless their intention to cover and include any and all structure, materials or acts that perform the claimed function, along with any all known or later developed equivalent structures, materials or acts for performing the claimed function.
“Pattern recognition” as used herein will generally mean any system which processes a signal that is generated by an object (e.g., representative of a pattern of returned or received impulses, waves or other physical property specific to and/or characteristic of and/or representative of that object) or is modified by interacting with an object, in order to determine to which one of a set of classes that the object belongs. Such a system might determine only that the object is or is not a member of one specified class, or it might attempt to assign the object to one of a larger set of specified classes, or find that it is not a member of any of the classes in the set. The signals processed are generally a series of electrical signals coming from transducers that are sensitive to acoustic (ultrasonic) or electromagnetic radiation (e.g., visible light, infrared radiation, radar, or any other frequency), although other sources of information including information from capacitance and electric field sensors are frequently included.
A trainable or a trained pattern recognition system as used herein generally means a pattern recognition system which is taught to recognize various patterns constituted within the signals by subjecting the system to a variety of examples. The most successful such system is the neural network or modular neural network. Thus, to generate the pattern recognition algorithm, test data is first obtained which constitutes a plurality of sets of returned waves, or wave patterns or other data, from an object (or from the space in which the object will be situated in the container or other storage facility or asset, e.g., the space in a truck or container) and an indication of the identity of that object, (e.g., a number of different objects are tested to obtain the unique wave patterns from each object). As such, the algorithm is generated, and stored in a computer processor, and which can later be applied to provide the identity of an object based on the wave or other pattern being received during use by a receiver connected to the processor and other information. For the purposes here, the identity of an object sometimes applies to not only the object itself but also to its location and/or orientation in a compartment, container or storage facility. For example, a rear facing child seat is a different object than a forward facing child seat, an out-of-position adult is a different object than a normally seated adult and an open container door is a different object than a closed container door.
Other means of pattern recognition exist where the training is done by the researcher including fuzzy logic and sensor fusion systems.
To “identify” as used herein will generally mean to determine that the object belongs to a particular set or class. The class may be one containing, for example, all rear facing child seats, one containing all human occupants, or all human occupants not sitting in a rear facing child seat or a box equal to or larger than a particular size depending on the purpose of the system. In the case where a particular person is to be recognized, the set or class will contain only a single element, i.e., the person to be recognized.
To “ascertain the identity of” as used herein with reference to an object will generally mean to determine the type or nature of the object (obtain information as to what the object is), i.e., that the object is a box, an adult, an occupied rear facing child seat, an occupied front facing child seat, an unoccupied rear facing child seat, an unoccupied front facing child seat, a child, a dog, a bag of groceries, etc.
“Transducer” as used herein will sometimes mean the combination of a transmitter and a receiver. In some cases, the same device will serve both as the transmitter and receiver while in others two separate devices adjacent to each other will be used. In some cases, a transmitter is not used and in such cases transducer will mean only a receiver. Transducers include, for example, capacitive, inductive, ultrasonic, electromagnetic (antenna, CCD, CMOS arrays), weight measuring, temperature, acceleration, chemical, sound or other sensing devices.
“Adaptation” as used herein represents the method by which a particular sensing system is designed and arranged for a particular vehicle container or other object. It includes such things as the process by which the number, kind and location of various transducers is determined. For pattern recognition systems, it includes the process by which the pattern recognition system is taught to recognize the desired patterns. In this connection, it will usually include (1) the method of training, (2) the makeup of the databases used for training, testing and validating the particular system, or, in the case of a neural network, the particular network architecture chosen, (3) the process by which environmental influences are incorporated into the system, and (4) any process for determining the pre-processing of the data or the post processing of the results of the pattern recognition system. The above list is illustrative and not exhaustive. Basically, adaptation includes all of the steps that are undertaken to adapt transducers and other sources of information to a particular vehicle, container, storage facility, structure or other object to create the system that accurately identifies and determines the location of an object in a vehicle, container or other object, for example.
For the purposes herein, a “neural network” is defined to include all such learning systems including cellular neural networks, support vector machines and other kernel-based learning systems and methods, cellular automata and all other pattern recognition methods and systems that learn. A “combination neural network” as used herein will generally apply to any combination of two or more neural networks that are either connected together or that analyze all or a portion of the input data. A combination neural network can be used to divide up tasks in solving a particular pattern recognition problem. For example, one neural network can be used to identify an object occupying a passenger compartment of an automobile or a shipping container and a second neural network can be used to determine the position of the object or its location with respect to the airbag or end of the container, for example, within the passenger compartment or container respectively. In another case, one neural network can be used merely to determine whether the data is similar to data upon which a main neural network has been trained or whether there is something radically different about this data and therefore that the data should not be analyzed. Combination neural networks can sometimes be implemented as cellular neural networks.